Contact Dr Stephen King
- Tel: +44 (0) 1234 754642
- Email: S.P.King@cranfield.ac.uk
- ORCID
Areas of expertise
- Instrumentation, Sensors and Measurement Science
- Vehicle Health Management
Background
Steve is currently a part-time senior Lecturer in Advanced Analytics having recently retired from Rolls-Royce (April 2020) where he was an Engineering Associate Fellow and EHM Specialist working within the Rolls-Royce Digital organisation. During his 41-year career at Rolls-Royce he held positions within the Measurement Engineering group, Electronics and Measurement Techniques department, Strategic Research Centre, Business Process Improvement Centre, Controls Engineering and System Design Engineering. Prior to this he worked for Electronic Flow Meters where he was responsible for the test and commissioning of flow measurement systems in the oil and gas industry.
His main interests is in the use of data mining and advanced analytical techniques for asset health monitoring applications. Steve holds a degree in Mathematics and Computer Science and a PhD in the application of expert systems for vibration analysis. In addition to being a Chartered Engineer, he is a Fellow member of both the Institution of Engineering and Technology and the Institute of Mathematics and its Applications.
Publications
Articles In Journals
- Li J, King S & Jennions IK. (2025). Intelligent multi-fault diagnosis for a simplified aircraft fuel system. Algorithms, 18(2)
- Upadhyay A, Li J, King S & Addepalli S. (2023). A deep-learning-based approach for aircraft engine defect detection. Machines, 11(2)
- Li J, King S & Jennions I. (2023). Intelligent fault diagnosis of an aircraft fuel system using machine learning - a literature review. Machines, 11(4)
- Dangut MD, Jennions IK, King S & Skaf Z. (2022). Application of deep reinforcement learning for extremely rare failure prediction in aircraft maintenance. Mechanical Systems and Signal Processing, 171(May)
- Dangut MD, Jennions IK, King S & Skaf Z. (2022). A rare failure detection model for aircraft predictive maintenance using a deep hybrid learning approach. Neural Computing and Applications, 35(4)
- Skliros C, Ali F, King S & Jennions I. (2021). Aircraft system-level diagnosis with emphasis on maintenance decisions. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 236(6)
- Lee GKK, Kasim H, Sirigina RP, How SSQ, King S, .... (2021). Smart Robust Feature Selection (SoFt) for imbalanced and heterogeneous data. Knowledge-Based Systems, 236(January)
- Hullait H, Leslie DS, Pavlidis NG & King S. (2021). Robust Function-on-Function Regression. Technometrics, 63(3)
- King S, Flint P & Sundaram S. (2010). Handling sparse data problems in the context of monitoring multiple parameters in complex systems. Insight - Non-Destructive Testing and Condition Monitoring, 52(8)
- King S, Bannister PR, Clifton DA & Tarassenko L. (2009). Probabilistic approach to the condition monitoring of aerospace engines. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 223(5)
- Hayton P, Utete S, King D, King S, Anuzis P, .... (2007). Static and dynamic novelty detection methods for jet engine health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1851)
- Nairac A, Townsend N, Carr R, King S, Cowley P, .... (1999). A System for the Analysis of Jet Engine Vibration Data. Integrated Computer-Aided Engineering, 6(1)
- Allwood RJ, King SP & Pitts NJ. (1996). The automatic interpretation of vibration data from gas turbines. The Aeronautical Journal, 100(993)